# -*- coding: utf-8 -*-
# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from collections import OrderedDict
import logging as std_logging
import re
from typing import (
    AsyncIterable,
    AsyncIterator,
    Awaitable,
    Callable,
    Dict,
    Mapping,
    MutableMapping,
    MutableSequence,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
)

from google.api_core import exceptions as core_exceptions
from google.api_core import gapic_v1
from google.api_core import retry_async as retries
from google.api_core.client_options import ClientOptions
from google.auth import credentials as ga_credentials  # type: ignore
from google.oauth2 import service_account  # type: ignore
import google.protobuf

from google.ai.generativelanguage_v1beta import gapic_version as package_version

try:
    OptionalRetry = Union[retries.AsyncRetry, gapic_v1.method._MethodDefault, None]
except AttributeError:  # pragma: NO COVER
    OptionalRetry = Union[retries.AsyncRetry, object, None]  # type: ignore

from google.longrunning import operations_pb2  # type: ignore

from google.ai.generativelanguage_v1beta.types import generative_service, safety
from google.ai.generativelanguage_v1beta.types import content
from google.ai.generativelanguage_v1beta.types import content as gag_content

from .client import GenerativeServiceClient
from .transports.base import DEFAULT_CLIENT_INFO, GenerativeServiceTransport
from .transports.grpc_asyncio import GenerativeServiceGrpcAsyncIOTransport

try:
    from google.api_core import client_logging  # type: ignore

    CLIENT_LOGGING_SUPPORTED = True  # pragma: NO COVER
except ImportError:  # pragma: NO COVER
    CLIENT_LOGGING_SUPPORTED = False

_LOGGER = std_logging.getLogger(__name__)


class GenerativeServiceAsyncClient:
    """API for using Large Models that generate multimodal content
    and have additional capabilities beyond text generation.
    """

    _client: GenerativeServiceClient

    # Copy defaults from the synchronous client for use here.
    # Note: DEFAULT_ENDPOINT is deprecated. Use _DEFAULT_ENDPOINT_TEMPLATE instead.
    DEFAULT_ENDPOINT = GenerativeServiceClient.DEFAULT_ENDPOINT
    DEFAULT_MTLS_ENDPOINT = GenerativeServiceClient.DEFAULT_MTLS_ENDPOINT
    _DEFAULT_ENDPOINT_TEMPLATE = GenerativeServiceClient._DEFAULT_ENDPOINT_TEMPLATE
    _DEFAULT_UNIVERSE = GenerativeServiceClient._DEFAULT_UNIVERSE

    cached_content_path = staticmethod(GenerativeServiceClient.cached_content_path)
    parse_cached_content_path = staticmethod(
        GenerativeServiceClient.parse_cached_content_path
    )
    model_path = staticmethod(GenerativeServiceClient.model_path)
    parse_model_path = staticmethod(GenerativeServiceClient.parse_model_path)
    common_billing_account_path = staticmethod(
        GenerativeServiceClient.common_billing_account_path
    )
    parse_common_billing_account_path = staticmethod(
        GenerativeServiceClient.parse_common_billing_account_path
    )
    common_folder_path = staticmethod(GenerativeServiceClient.common_folder_path)
    parse_common_folder_path = staticmethod(
        GenerativeServiceClient.parse_common_folder_path
    )
    common_organization_path = staticmethod(
        GenerativeServiceClient.common_organization_path
    )
    parse_common_organization_path = staticmethod(
        GenerativeServiceClient.parse_common_organization_path
    )
    common_project_path = staticmethod(GenerativeServiceClient.common_project_path)
    parse_common_project_path = staticmethod(
        GenerativeServiceClient.parse_common_project_path
    )
    common_location_path = staticmethod(GenerativeServiceClient.common_location_path)
    parse_common_location_path = staticmethod(
        GenerativeServiceClient.parse_common_location_path
    )

    @classmethod
    def from_service_account_info(cls, info: dict, *args, **kwargs):
        """Creates an instance of this client using the provided credentials
            info.

        Args:
            info (dict): The service account private key info.
            args: Additional arguments to pass to the constructor.
            kwargs: Additional arguments to pass to the constructor.

        Returns:
            GenerativeServiceAsyncClient: The constructed client.
        """
        return GenerativeServiceClient.from_service_account_info.__func__(GenerativeServiceAsyncClient, info, *args, **kwargs)  # type: ignore

    @classmethod
    def from_service_account_file(cls, filename: str, *args, **kwargs):
        """Creates an instance of this client using the provided credentials
            file.

        Args:
            filename (str): The path to the service account private key json
                file.
            args: Additional arguments to pass to the constructor.
            kwargs: Additional arguments to pass to the constructor.

        Returns:
            GenerativeServiceAsyncClient: The constructed client.
        """
        return GenerativeServiceClient.from_service_account_file.__func__(GenerativeServiceAsyncClient, filename, *args, **kwargs)  # type: ignore

    from_service_account_json = from_service_account_file

    @classmethod
    def get_mtls_endpoint_and_cert_source(
        cls, client_options: Optional[ClientOptions] = None
    ):
        """Return the API endpoint and client cert source for mutual TLS.

        The client cert source is determined in the following order:
        (1) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is not "true", the
        client cert source is None.
        (2) if `client_options.client_cert_source` is provided, use the provided one; if the
        default client cert source exists, use the default one; otherwise the client cert
        source is None.

        The API endpoint is determined in the following order:
        (1) if `client_options.api_endpoint` if provided, use the provided one.
        (2) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is "always", use the
        default mTLS endpoint; if the environment variable is "never", use the default API
        endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise
        use the default API endpoint.

        More details can be found at https://google.aip.dev/auth/4114.

        Args:
            client_options (google.api_core.client_options.ClientOptions): Custom options for the
                client. Only the `api_endpoint` and `client_cert_source` properties may be used
                in this method.

        Returns:
            Tuple[str, Callable[[], Tuple[bytes, bytes]]]: returns the API endpoint and the
                client cert source to use.

        Raises:
            google.auth.exceptions.MutualTLSChannelError: If any errors happen.
        """
        return GenerativeServiceClient.get_mtls_endpoint_and_cert_source(client_options)  # type: ignore

    @property
    def transport(self) -> GenerativeServiceTransport:
        """Returns the transport used by the client instance.

        Returns:
            GenerativeServiceTransport: The transport used by the client instance.
        """
        return self._client.transport

    @property
    def api_endpoint(self):
        """Return the API endpoint used by the client instance.

        Returns:
            str: The API endpoint used by the client instance.
        """
        return self._client._api_endpoint

    @property
    def universe_domain(self) -> str:
        """Return the universe domain used by the client instance.

        Returns:
            str: The universe domain used
                by the client instance.
        """
        return self._client._universe_domain

    get_transport_class = GenerativeServiceClient.get_transport_class

    def __init__(
        self,
        *,
        credentials: Optional[ga_credentials.Credentials] = None,
        transport: Optional[
            Union[
                str,
                GenerativeServiceTransport,
                Callable[..., GenerativeServiceTransport],
            ]
        ] = "grpc_asyncio",
        client_options: Optional[ClientOptions] = None,
        client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
    ) -> None:
        """Instantiates the generative service async client.

        Args:
            credentials (Optional[google.auth.credentials.Credentials]): The
                authorization credentials to attach to requests. These
                credentials identify the application to the service; if none
                are specified, the client will attempt to ascertain the
                credentials from the environment.
            transport (Optional[Union[str,GenerativeServiceTransport,Callable[..., GenerativeServiceTransport]]]):
                The transport to use, or a Callable that constructs and returns a new transport to use.
                If a Callable is given, it will be called with the same set of initialization
                arguments as used in the GenerativeServiceTransport constructor.
                If set to None, a transport is chosen automatically.
            client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]):
                Custom options for the client.

                1. The ``api_endpoint`` property can be used to override the
                default endpoint provided by the client when ``transport`` is
                not explicitly provided. Only if this property is not set and
                ``transport`` was not explicitly provided, the endpoint is
                determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment
                variable, which have one of the following values:
                "always" (always use the default mTLS endpoint), "never" (always
                use the default regular endpoint) and "auto" (auto-switch to the
                default mTLS endpoint if client certificate is present; this is
                the default value).

                2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable
                is "true", then the ``client_cert_source`` property can be used
                to provide a client certificate for mTLS transport. If
                not provided, the default SSL client certificate will be used if
                present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not
                set, no client certificate will be used.

                3. The ``universe_domain`` property can be used to override the
                default "googleapis.com" universe. Note that ``api_endpoint``
                property still takes precedence; and ``universe_domain`` is
                currently not supported for mTLS.

            client_info (google.api_core.gapic_v1.client_info.ClientInfo):
                The client info used to send a user-agent string along with
                API requests. If ``None``, then default info will be used.
                Generally, you only need to set this if you're developing
                your own client library.

        Raises:
            google.auth.exceptions.MutualTlsChannelError: If mutual TLS transport
                creation failed for any reason.
        """
        self._client = GenerativeServiceClient(
            credentials=credentials,
            transport=transport,
            client_options=client_options,
            client_info=client_info,
        )

        if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(
            std_logging.DEBUG
        ):  # pragma: NO COVER
            _LOGGER.debug(
                "Created client `google.ai.generativelanguage_v1beta.GenerativeServiceAsyncClient`.",
                extra={
                    "serviceName": "google.ai.generativelanguage.v1beta.GenerativeService",
                    "universeDomain": getattr(
                        self._client._transport._credentials, "universe_domain", ""
                    ),
                    "credentialsType": f"{type(self._client._transport._credentials).__module__}.{type(self._client._transport._credentials).__qualname__}",
                    "credentialsInfo": getattr(
                        self.transport._credentials, "get_cred_info", lambda: None
                    )(),
                }
                if hasattr(self._client._transport, "_credentials")
                else {
                    "serviceName": "google.ai.generativelanguage.v1beta.GenerativeService",
                    "credentialsType": None,
                },
            )

    async def generate_content(
        self,
        request: Optional[
            Union[generative_service.GenerateContentRequest, dict]
        ] = None,
        *,
        model: Optional[str] = None,
        contents: Optional[MutableSequence[content.Content]] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> generative_service.GenerateContentResponse:
        r"""Generates a model response given an input
        ``GenerateContentRequest``. Refer to the `text generation
        guide <https://ai.google.dev/gemini-api/docs/text-generation>`__
        for detailed usage information. Input capabilities differ
        between models, including tuned models. Refer to the `model
        guide <https://ai.google.dev/gemini-api/docs/models/gemini>`__
        and `tuning
        guide <https://ai.google.dev/gemini-api/docs/model-tuning>`__
        for details.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            async def sample_generate_content():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.GenerateContentRequest(
                    model="model_value",
                )

                # Make the request
                response = await client.generate_content(request=request)

                # Handle the response
                print(response)

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.GenerateContentRequest, dict]]):
                The request object. Request to generate a completion from
                the model. NEXT ID: 18
            model (:class:`str`):
                Required. The name of the ``Model`` to use for
                generating the completion.

                Format: ``models/{model}``.

                This corresponds to the ``model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            contents (:class:`MutableSequence[google.ai.generativelanguage_v1beta.types.Content]`):
                Required. The content of the current conversation with
                the model.

                For single-turn queries, this is a single instance. For
                multi-turn queries like
                `chat <https://ai.google.dev/gemini-api/docs/text-generation#chat>`__,
                this is a repeated field that contains the conversation
                history and the latest request.

                This corresponds to the ``contents`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            google.ai.generativelanguage_v1beta.types.GenerateContentResponse:
                Response from the model supporting multiple candidate
                responses.

                   Safety ratings and content filtering are reported for
                   both prompt in
                   GenerateContentResponse.prompt_feedback and for each
                   candidate in finish_reason and in safety_ratings. The
                   API: - Returns either all requested candidates or
                   none of them - Returns no candidates at all only if
                   there was something wrong with the prompt (check
                   prompt_feedback) - Reports feedback on each candidate
                   in finish_reason and safety_ratings.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        flattened_params = [model, contents]
        has_flattened_params = (
            len([param for param in flattened_params if param is not None]) > 0
        )
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, generative_service.GenerateContentRequest):
            request = generative_service.GenerateContentRequest(request)

        # If we have keyword arguments corresponding to fields on the
        # request, apply these.
        if model is not None:
            request.model = model
        if contents:
            request.contents.extend(contents)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._client._transport._wrapped_methods[
            self._client._transport.generate_content
        ]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("model", request.model),)),
        )

        # Validate the universe domain.
        self._client._validate_universe_domain()

        # Send the request.
        response = await rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    async def generate_answer(
        self,
        request: Optional[Union[generative_service.GenerateAnswerRequest, dict]] = None,
        *,
        model: Optional[str] = None,
        contents: Optional[MutableSequence[content.Content]] = None,
        safety_settings: Optional[MutableSequence[safety.SafetySetting]] = None,
        answer_style: Optional[
            generative_service.GenerateAnswerRequest.AnswerStyle
        ] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> generative_service.GenerateAnswerResponse:
        r"""Generates a grounded answer from the model given an input
        ``GenerateAnswerRequest``.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            async def sample_generate_answer():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.GenerateAnswerRequest(
                    model="model_value",
                    answer_style="VERBOSE",
                )

                # Make the request
                response = await client.generate_answer(request=request)

                # Handle the response
                print(response)

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.GenerateAnswerRequest, dict]]):
                The request object. Request to generate a grounded answer from the
                ``Model``.
            model (:class:`str`):
                Required. The name of the ``Model`` to use for
                generating the grounded response.

                Format: ``model=models/{model}``.

                This corresponds to the ``model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            contents (:class:`MutableSequence[google.ai.generativelanguage_v1beta.types.Content]`):
                Required. The content of the current conversation with
                the ``Model``. For single-turn queries, this is a single
                question to answer. For multi-turn queries, this is a
                repeated field that contains conversation history and
                the last ``Content`` in the list containing the
                question.

                Note: ``GenerateAnswer`` only supports queries in
                English.

                This corresponds to the ``contents`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            safety_settings (:class:`MutableSequence[google.ai.generativelanguage_v1beta.types.SafetySetting]`):
                Optional. A list of unique ``SafetySetting`` instances
                for blocking unsafe content.

                This will be enforced on the
                ``GenerateAnswerRequest.contents`` and
                ``GenerateAnswerResponse.candidate``. There should not
                be more than one setting for each ``SafetyCategory``
                type. The API will block any contents and responses that
                fail to meet the thresholds set by these settings. This
                list overrides the default settings for each
                ``SafetyCategory`` specified in the safety_settings. If
                there is no ``SafetySetting`` for a given
                ``SafetyCategory`` provided in the list, the API will
                use the default safety setting for that category. Harm
                categories HARM_CATEGORY_HATE_SPEECH,
                HARM_CATEGORY_SEXUALLY_EXPLICIT,
                HARM_CATEGORY_DANGEROUS_CONTENT,
                HARM_CATEGORY_HARASSMENT are supported. Refer to the
                `guide <https://ai.google.dev/gemini-api/docs/safety-settings>`__
                for detailed information on available safety settings.
                Also refer to the `Safety
                guidance <https://ai.google.dev/gemini-api/docs/safety-guidance>`__
                to learn how to incorporate safety considerations in
                your AI applications.

                This corresponds to the ``safety_settings`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            answer_style (:class:`google.ai.generativelanguage_v1beta.types.GenerateAnswerRequest.AnswerStyle`):
                Required. Style in which answers
                should be returned.

                This corresponds to the ``answer_style`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            google.ai.generativelanguage_v1beta.types.GenerateAnswerResponse:
                Response from the model for a
                grounded answer.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        flattened_params = [model, contents, safety_settings, answer_style]
        has_flattened_params = (
            len([param for param in flattened_params if param is not None]) > 0
        )
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, generative_service.GenerateAnswerRequest):
            request = generative_service.GenerateAnswerRequest(request)

        # If we have keyword arguments corresponding to fields on the
        # request, apply these.
        if model is not None:
            request.model = model
        if answer_style is not None:
            request.answer_style = answer_style
        if contents:
            request.contents.extend(contents)
        if safety_settings:
            request.safety_settings.extend(safety_settings)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._client._transport._wrapped_methods[
            self._client._transport.generate_answer
        ]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("model", request.model),)),
        )

        # Validate the universe domain.
        self._client._validate_universe_domain()

        # Send the request.
        response = await rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def stream_generate_content(
        self,
        request: Optional[
            Union[generative_service.GenerateContentRequest, dict]
        ] = None,
        *,
        model: Optional[str] = None,
        contents: Optional[MutableSequence[content.Content]] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> Awaitable[AsyncIterable[generative_service.GenerateContentResponse]]:
        r"""Generates a `streamed
        response <https://ai.google.dev/gemini-api/docs/text-generation?lang=python#generate-a-text-stream>`__
        from the model given an input ``GenerateContentRequest``.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            async def sample_stream_generate_content():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.GenerateContentRequest(
                    model="model_value",
                )

                # Make the request
                stream = await client.stream_generate_content(request=request)

                # Handle the response
                async for response in stream:
                    print(response)

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.GenerateContentRequest, dict]]):
                The request object. Request to generate a completion from
                the model. NEXT ID: 18
            model (:class:`str`):
                Required. The name of the ``Model`` to use for
                generating the completion.

                Format: ``models/{model}``.

                This corresponds to the ``model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            contents (:class:`MutableSequence[google.ai.generativelanguage_v1beta.types.Content]`):
                Required. The content of the current conversation with
                the model.

                For single-turn queries, this is a single instance. For
                multi-turn queries like
                `chat <https://ai.google.dev/gemini-api/docs/text-generation#chat>`__,
                this is a repeated field that contains the conversation
                history and the latest request.

                This corresponds to the ``contents`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            AsyncIterable[google.ai.generativelanguage_v1beta.types.GenerateContentResponse]:
                Response from the model supporting multiple candidate
                responses.

                   Safety ratings and content filtering are reported for
                   both prompt in
                   GenerateContentResponse.prompt_feedback and for each
                   candidate in finish_reason and in safety_ratings. The
                   API: - Returns either all requested candidates or
                   none of them - Returns no candidates at all only if
                   there was something wrong with the prompt (check
                   prompt_feedback) - Reports feedback on each candidate
                   in finish_reason and safety_ratings.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        flattened_params = [model, contents]
        has_flattened_params = (
            len([param for param in flattened_params if param is not None]) > 0
        )
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, generative_service.GenerateContentRequest):
            request = generative_service.GenerateContentRequest(request)

        # If we have keyword arguments corresponding to fields on the
        # request, apply these.
        if model is not None:
            request.model = model
        if contents:
            request.contents.extend(contents)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._client._transport._wrapped_methods[
            self._client._transport.stream_generate_content
        ]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("model", request.model),)),
        )

        # Validate the universe domain.
        self._client._validate_universe_domain()

        # Send the request.
        response = rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    async def embed_content(
        self,
        request: Optional[Union[generative_service.EmbedContentRequest, dict]] = None,
        *,
        model: Optional[str] = None,
        content: Optional[gag_content.Content] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> generative_service.EmbedContentResponse:
        r"""Generates a text embedding vector from the input ``Content``
        using the specified `Gemini Embedding
        model <https://ai.google.dev/gemini-api/docs/models/gemini#text-embedding>`__.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            async def sample_embed_content():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.EmbedContentRequest(
                    model="model_value",
                )

                # Make the request
                response = await client.embed_content(request=request)

                # Handle the response
                print(response)

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.EmbedContentRequest, dict]]):
                The request object. Request containing the ``Content`` for the model to
                embed.
            model (:class:`str`):
                Required. The model's resource name. This serves as an
                ID for the Model to use.

                This name should match a model name returned by the
                ``ListModels`` method.

                Format: ``models/{model}``

                This corresponds to the ``model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            content (:class:`google.ai.generativelanguage_v1beta.types.Content`):
                Required. The content to embed. Only the ``parts.text``
                fields will be counted.

                This corresponds to the ``content`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            google.ai.generativelanguage_v1beta.types.EmbedContentResponse:
                The response to an EmbedContentRequest.
        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        flattened_params = [model, content]
        has_flattened_params = (
            len([param for param in flattened_params if param is not None]) > 0
        )
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, generative_service.EmbedContentRequest):
            request = generative_service.EmbedContentRequest(request)

        # If we have keyword arguments corresponding to fields on the
        # request, apply these.
        if model is not None:
            request.model = model
        if content is not None:
            request.content = content

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._client._transport._wrapped_methods[
            self._client._transport.embed_content
        ]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("model", request.model),)),
        )

        # Validate the universe domain.
        self._client._validate_universe_domain()

        # Send the request.
        response = await rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    async def batch_embed_contents(
        self,
        request: Optional[
            Union[generative_service.BatchEmbedContentsRequest, dict]
        ] = None,
        *,
        model: Optional[str] = None,
        requests: Optional[
            MutableSequence[generative_service.EmbedContentRequest]
        ] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> generative_service.BatchEmbedContentsResponse:
        r"""Generates multiple embedding vectors from the input ``Content``
        which consists of a batch of strings represented as
        ``EmbedContentRequest`` objects.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            async def sample_batch_embed_contents():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

                # Initialize request argument(s)
                requests = generativelanguage_v1beta.EmbedContentRequest()
                requests.model = "model_value"

                request = generativelanguage_v1beta.BatchEmbedContentsRequest(
                    model="model_value",
                    requests=requests,
                )

                # Make the request
                response = await client.batch_embed_contents(request=request)

                # Handle the response
                print(response)

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.BatchEmbedContentsRequest, dict]]):
                The request object. Batch request to get embeddings from
                the model for a list of prompts.
            model (:class:`str`):
                Required. The model's resource name. This serves as an
                ID for the Model to use.

                This name should match a model name returned by the
                ``ListModels`` method.

                Format: ``models/{model}``

                This corresponds to the ``model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            requests (:class:`MutableSequence[google.ai.generativelanguage_v1beta.types.EmbedContentRequest]`):
                Required. Embed requests for the batch. The model in
                each of these requests must match the model specified
                ``BatchEmbedContentsRequest.model``.

                This corresponds to the ``requests`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            google.ai.generativelanguage_v1beta.types.BatchEmbedContentsResponse:
                The response to a BatchEmbedContentsRequest.
        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        flattened_params = [model, requests]
        has_flattened_params = (
            len([param for param in flattened_params if param is not None]) > 0
        )
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, generative_service.BatchEmbedContentsRequest):
            request = generative_service.BatchEmbedContentsRequest(request)

        # If we have keyword arguments corresponding to fields on the
        # request, apply these.
        if model is not None:
            request.model = model
        if requests:
            request.requests.extend(requests)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._client._transport._wrapped_methods[
            self._client._transport.batch_embed_contents
        ]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("model", request.model),)),
        )

        # Validate the universe domain.
        self._client._validate_universe_domain()

        # Send the request.
        response = await rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    async def count_tokens(
        self,
        request: Optional[Union[generative_service.CountTokensRequest, dict]] = None,
        *,
        model: Optional[str] = None,
        contents: Optional[MutableSequence[content.Content]] = None,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> generative_service.CountTokensResponse:
        r"""Runs a model's tokenizer on input ``Content`` and returns the
        token count. Refer to the `tokens
        guide <https://ai.google.dev/gemini-api/docs/tokens>`__ to learn
        more about tokens.

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            async def sample_count_tokens():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

                # Initialize request argument(s)
                request = generativelanguage_v1beta.CountTokensRequest(
                    model="model_value",
                )

                # Make the request
                response = await client.count_tokens(request=request)

                # Handle the response
                print(response)

        Args:
            request (Optional[Union[google.ai.generativelanguage_v1beta.types.CountTokensRequest, dict]]):
                The request object. Counts the number of tokens in the ``prompt`` sent to a
                model.

                Models may tokenize text differently, so each model may
                return a different ``token_count``.
            model (:class:`str`):
                Required. The model's resource name. This serves as an
                ID for the Model to use.

                This name should match a model name returned by the
                ``ListModels`` method.

                Format: ``models/{model}``

                This corresponds to the ``model`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            contents (:class:`MutableSequence[google.ai.generativelanguage_v1beta.types.Content]`):
                Optional. The input given to the model as a prompt. This
                field is ignored when ``generate_content_request`` is
                set.

                This corresponds to the ``contents`` field
                on the ``request`` instance; if ``request`` is provided, this
                should not be set.
            retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            google.ai.generativelanguage_v1beta.types.CountTokensResponse:
                A response from CountTokens.

                   It returns the model's token_count for the prompt.

        """
        # Create or coerce a protobuf request object.
        # - Quick check: If we got a request object, we should *not* have
        #   gotten any keyword arguments that map to the request.
        flattened_params = [model, contents]
        has_flattened_params = (
            len([param for param in flattened_params if param is not None]) > 0
        )
        if request is not None and has_flattened_params:
            raise ValueError(
                "If the `request` argument is set, then none of "
                "the individual field arguments should be set."
            )

        # - Use the request object if provided (there's no risk of modifying the input as
        #   there are no flattened fields), or create one.
        if not isinstance(request, generative_service.CountTokensRequest):
            request = generative_service.CountTokensRequest(request)

        # If we have keyword arguments corresponding to fields on the
        # request, apply these.
        if model is not None:
            request.model = model
        if contents:
            request.contents.extend(contents)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._client._transport._wrapped_methods[
            self._client._transport.count_tokens
        ]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("model", request.model),)),
        )

        # Validate the universe domain.
        self._client._validate_universe_domain()

        # Send the request.
        response = await rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    def bidi_generate_content(
        self,
        requests: Optional[
            AsyncIterator[generative_service.BidiGenerateContentClientMessage]
        ] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> Awaitable[AsyncIterable[generative_service.BidiGenerateContentServerMessage]]:
        r"""Low-Latency bidirectional streaming API that supports
        audio and video streaming inputs can produce multimodal
        output streams (audio and text).

        .. code-block:: python

            # This snippet has been automatically generated and should be regarded as a
            # code template only.
            # It will require modifications to work:
            # - It may require correct/in-range values for request initialization.
            # - It may require specifying regional endpoints when creating the service
            #   client as shown in:
            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
            from google.ai import generativelanguage_v1beta

            async def sample_bidi_generate_content():
                # Create a client
                client = generativelanguage_v1beta.GenerativeServiceAsyncClient()

                # Initialize request argument(s)
                setup = generativelanguage_v1beta.BidiGenerateContentSetup()
                setup.model = "model_value"

                request = generativelanguage_v1beta.BidiGenerateContentClientMessage(
                    setup=setup,
                )

                # This method expects an iterator which contains
                # 'generativelanguage_v1beta.BidiGenerateContentClientMessage' objects
                # Here we create a generator that yields a single `request` for
                # demonstrative purposes.
                requests = [request]

                def request_generator():
                    for request in requests:
                        yield request

                # Make the request
                stream = await client.bidi_generate_content(requests=request_generator())

                # Handle the response
                async for response in stream:
                    print(response)

        Args:
            requests (AsyncIterator[`google.ai.generativelanguage_v1beta.types.BidiGenerateContentClientMessage`]):
                The request object AsyncIterator. Messages sent by the client in the
                BidiGenerateContent call.
            retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
                should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.

        Returns:
            AsyncIterable[google.ai.generativelanguage_v1beta.types.BidiGenerateContentServerMessage]:
                Response message for the
                BidiGenerateContent call.

        """

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self._client._transport._wrapped_methods[
            self._client._transport.bidi_generate_content
        ]

        # Validate the universe domain.
        self._client._validate_universe_domain()

        # Send the request.
        response = rpc(
            requests,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    async def list_operations(
        self,
        request: Optional[operations_pb2.ListOperationsRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> operations_pb2.ListOperationsResponse:
        r"""Lists operations that match the specified filter in the request.

        Args:
            request (:class:`~.operations_pb2.ListOperationsRequest`):
                The request object. Request message for
                `ListOperations` method.
            retry (google.api_core.retry_async.AsyncRetry): Designation of what errors,
                    if any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.
        Returns:
            ~.operations_pb2.ListOperationsResponse:
                Response message for ``ListOperations`` method.
        """
        # Create or coerce a protobuf request object.
        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = operations_pb2.ListOperationsRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self.transport._wrapped_methods[self._client._transport.list_operations]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._client._validate_universe_domain()

        # Send the request.
        response = await rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    async def get_operation(
        self,
        request: Optional[operations_pb2.GetOperationRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> operations_pb2.Operation:
        r"""Gets the latest state of a long-running operation.

        Args:
            request (:class:`~.operations_pb2.GetOperationRequest`):
                The request object. Request message for
                `GetOperation` method.
            retry (google.api_core.retry_async.AsyncRetry): Designation of what errors,
                    if any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.
        Returns:
            ~.operations_pb2.Operation:
                An ``Operation`` object.
        """
        # Create or coerce a protobuf request object.
        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = operations_pb2.GetOperationRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self.transport._wrapped_methods[self._client._transport.get_operation]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._client._validate_universe_domain()

        # Send the request.
        response = await rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

        # Done; return the response.
        return response

    async def delete_operation(
        self,
        request: Optional[operations_pb2.DeleteOperationRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> None:
        r"""Deletes a long-running operation.

        This method indicates that the client is no longer interested
        in the operation result. It does not cancel the operation.
        If the server doesn't support this method, it returns
        `google.rpc.Code.UNIMPLEMENTED`.

        Args:
            request (:class:`~.operations_pb2.DeleteOperationRequest`):
                The request object. Request message for
                `DeleteOperation` method.
            retry (google.api_core.retry_async.AsyncRetry): Designation of what errors,
                    if any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.
        Returns:
            None
        """
        # Create or coerce a protobuf request object.
        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = operations_pb2.DeleteOperationRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self.transport._wrapped_methods[self._client._transport.delete_operation]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._client._validate_universe_domain()

        # Send the request.
        await rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

    async def cancel_operation(
        self,
        request: Optional[operations_pb2.CancelOperationRequest] = None,
        *,
        retry: OptionalRetry = gapic_v1.method.DEFAULT,
        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
    ) -> None:
        r"""Starts asynchronous cancellation on a long-running operation.

        The server makes a best effort to cancel the operation, but success
        is not guaranteed.  If the server doesn't support this method, it returns
        `google.rpc.Code.UNIMPLEMENTED`.

        Args:
            request (:class:`~.operations_pb2.CancelOperationRequest`):
                The request object. Request message for
                `CancelOperation` method.
            retry (google.api_core.retry_async.AsyncRetry): Designation of what errors,
                    if any, should be retried.
            timeout (float): The timeout for this request.
            metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be
                sent along with the request as metadata. Normally, each value must be of type `str`,
                but for metadata keys ending with the suffix `-bin`, the corresponding values must
                be of type `bytes`.
        Returns:
            None
        """
        # Create or coerce a protobuf request object.
        # The request isn't a proto-plus wrapped type,
        # so it must be constructed via keyword expansion.
        if isinstance(request, dict):
            request = operations_pb2.CancelOperationRequest(**request)

        # Wrap the RPC method; this adds retry and timeout information,
        # and friendly error handling.
        rpc = self.transport._wrapped_methods[self._client._transport.cancel_operation]

        # Certain fields should be provided within the metadata header;
        # add these here.
        metadata = tuple(metadata) + (
            gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
        )

        # Validate the universe domain.
        self._client._validate_universe_domain()

        # Send the request.
        await rpc(
            request,
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )

    async def __aenter__(self) -> "GenerativeServiceAsyncClient":
        return self

    async def __aexit__(self, exc_type, exc, tb):
        await self.transport.close()


DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
    gapic_version=package_version.__version__
)

if hasattr(DEFAULT_CLIENT_INFO, "protobuf_runtime_version"):  # pragma: NO COVER
    DEFAULT_CLIENT_INFO.protobuf_runtime_version = google.protobuf.__version__


__all__ = ("GenerativeServiceAsyncClient",)
